Aggregation functions for recommender systems

Recommender Systems Handbook, Second Edition

Volumn , Issue , 2015, Pages 777-808

  Beliakov, Gleb ^a   Calvo, Tomasa ^b   James, Simon ^a  

^a DEAKIN UNIVERSITY   (Australia)

^b UNIVERSITY OF ALCALÁ   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

AGGREGATION FUNCTIONS; METHODS OF CONSTRUCTIONS; RECOMMENDATION MECHANISM; WEIGHTED AVERAGES;

RECOMMENDER SYSTEMS;

EID: 84956766956     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-1-4899-7637-6_23     Document Type: Chapter

References (51)

1. Adomavicius, G., Sankaranarayanan, R., Sen, S., and Tuzhilin, A.: Incorporating contextual information in recommender systems using a multi-dimensional approach, ACM Transactions on information systems, 23(1), 103–145 (2005)
2. Adomavicius, G. and Kwon, Y.: New Recommendation Techniques for Multicriteria Rating Systems. IEEE Intelligent Systems, 22(3), 48–55 (2007)
3. Ahn, H.J.: A new similarity measure for collaborative filtering to alleviate the new user coldstarting problem. Information Sciences, 178, 37–51 (2008)
4. Al-Shamri, M.Y.H. and Bharadwaj, K.K.: Fuzzy-genetic approach to recommender systems based on a novel hybrid user model. Expert Systems with Applications, 35, 1386–1399 (2008)
5. Atanassov, K.: Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20, 87–96 (1986)
6. Balabanovic, M. and Shoham, Y.: Fab: Content-Based, Collaborative Recommendation. Comm. ACM, 40(3), 66–72 (1997)
7. Beliakov, G.: Monotone approximation of aggregation operators using least squares splines. Int. J. of Uncertainty, Fuzziness and Knowledge-Based Systems, 10, 659–676 (2002)
8. Beliakov, G.: How to build aggregation operators from data? Int. J. Intelligent Systems, 18, 903–923 (2003)
9. Beliakov, G.: FMTools package, version 1.0, http://www.deakin.edu.au/~gleb/aotool.html, (2007)
10. Beliakov, G., Bustince, H., Goswami, D.P., Mukherjee, U.K. and Pal, N.R.: On averaging operators for Atanassov’s intuitionistic fuzzy sets. Information Sciences, 181, 1116–1124 (2011)
11. Beliakov, G. and Calvo, T.: Construction of Aggregation OperatorsWith Noble Reinforcement. IEEE Transactions on Fuzzy Systems, 15(6), 1209–1218 (2007)
12. Beliakov, G., Pradera, A. and Calvo, T.: Aggregation Functions: A guide for practitioners. Springer, Heidelberg, Berlin, New York (2007)
13. Beliakov, G., Calvo, T. and James, S: On Lipschitz properties of generated aggregation functions. Fuzzy Sets and Systems, 161, 1437–1447 (2009)
14. Beliakov, G. and James, S: Using Choquet Integrals for kNN Approximation and Classification. In Gary G. Feng (ed.), 2008 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2008), 1311–1317 (2008)
15. Beliakov, G. and James, S: On extending generalized Bonferroni means to Atanassov orthopairs in decision making contexts, Fuzzy Sets and Systems, 211, 84–98 (2013)
16. Beliakov, G. and James, S: Stability of weighted penalty-based aggregation functions, Fuzzy Sets and Systems, 226, 1–18 (2013)
17. Bobadilla, J., Ortega, F., Hernando, A. and Gutiérrez, A.: Recommender systems survey, Knowledge-Based Systems, 46, 109–132 (2013)
18. Bolton, J., Gader, P. and Wilson, J.N.: Discrete Choquet Integral as a Distance Metric. IEEE Trans. on Fuzzy Systems, 16(4), 1107–1110 (2008)
19. Buchanan, B. and Shortliffe, E.: Rule-based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Addison-Wesley, Reading, MA (1984)
20. Bullen, P.S.: Handbook of Means and Their Inequalities. Kluwer, Dordrecht (2003)
21. Burke, R.: Hybrid Recommender Systems: Survey and Experiments, User Modeling and Useradapted interaction, 12(4), 331–370 (2002)
22. Calvo, T. and Beliakov, G.: Aggregation functions based on penalties, Fuzzy Sets and Systems, 161, 1420–1436 (2010)
23. Calvo, T., Kolesárová, A., Komorníková, M. and Mesiar, R.: Aggregation operators: properties, classes and construction methods. In: Calvo, T., Mayor, G. and Mesiar, R. (eds.) Aggregation Operators. New Trends and Applications, pp. 3–104. Physica-Verlag, Heidelberg, New York (2002)
24. Chen, Y.L. and Cheng, L.C.: A novel collaborative filtering approach for recommending ranked items. Expert Systems with Applications, 34, 2396–2405 (2008)
25. Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D. and Sartin, M.: Combining Content-Based and Collaborative Filters in an Online Newspaper. In Proceedings of SIGIR 99 Workshop on Recommender Systems: Algorithms and Evaluation, Berkeley, CA (1999)
26. Campos, L.M.d., Fernández-Luna, J.M. and Huete, J.F.: A collaborative recommender system based on probabilistic inference from fuzzy observations. Fuzzy Sets and Systems, 159, 1554–1576 (2008)
27. Dubois, D. and Prade, H.: Fuzzy Sets and Systems: Theory and Applications. Academic Press, New York (1980)
28. Dubois, D. and Prade, H.: Fundamentals of Fuzzy Sets. Kluwer, Boston (2000)
29. Dubois, D., Hllermeier, E., and Prade, H.: Fuzzy methods for case-based recommendation and decision support. J. Intell. Inform. Systems, 27(2), 95–115 (2006)
30. Duda, R. Hart, P. and Nilsson, N.: Subjective Bayesian methods for rule-based inference systems. In Proc. Nat. Comput. Conf. (AFIPS), volume 45, 1075–1082 (1976)
31. Garcia, I., Sebastia, L. and Onaindia, E.: On the design of individual and group recommender systems for tourism, Expert Systems with Applications, 38, 7683–7692 (2011)
32. Grabisch, M., Kojadinovic, I., and Meyer, P.: A review of methods for capacity identification in Choquet integral based multi-attribute utility theory: Applications of the Kappalab R package. European Journal of Operational Research. 186, 766–785 (2008)
33. Kaymak, U. and van Nauta Lemke, H.R.: Selecting an aggregation operator for fuzzy decision making. In 3rd IEEE Intl. Conf. on Fuzzy Systems, volume 2, 1418–1422 (1994)
34. Klement, E.P., Mesiar, R. and Pap, E.: Triangular Norms. Kluwer, Dordrecht, (2000)
35. Kojadinovic, I. and Grabisch, M.: Non additive measure and integral manipulation functions, R package version 0.2, http://www.polytech.univ-nantes.fr/kappalab, (2005)
36. Krawczyk, H., Knopa, R., Lipczynska, K., and Lipczynski, M.: Web-based Endoscopy Recommender System - ERS. In International Conference on Parallel Computing in Electrical Engineering (PARELEC’00), Quebec, Canada, 257–261 (2000)
37. Linden, G., Smith, B., and York, J.: Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1), 76–80 (2003)
38. Mayor, G., and Calvo, T.: Extended aggregation functions. In IFSA’97, volume 1, Prague, 281–285 (1997)
39. Miller, B.N., Albert, I., Lam, S.K., Konstan, J.A., and Riedl, J.: MovieLens unplugged: experiences with an occasionally connected recommender system. In Proceedings of the 8th international ACM conference on Intelligent user interfaces, Miami, USA, 263–266 (2003)
40. Resnick, P., Iakovou, N., Sushak, M., Bergstrom, P., and Riedl, J.: GroupLens: An open architecture for collaborative filtering of Netnews. In Proceedings of ACM conference on computer supported cooperative work, Chapel Hill, NC, 175–186 (1994)
41. Rojas, K., Gómez, D., Montero, J., and Rodríguez, J. T.: Strictly stable families of aggregation operators, Fuzzy Sets and Systems, 228, 44–63 (2013)
42. Salton, G. and Mc Gill, M.: Introduction to Modern Information retrieval. McGraw Hill, New York (1983)
43. Schafer, J.B., Konstan, J.A., and Riedl, J.: E-commerce recommendation applications, Data Mining and Knowledge Discover, 5, 115–153 (2001)
44. Santini, S. and Jain, R.: Similarity Measures. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(9), 871–883 (1999)
45. Ujjin, S., and Bentley, P.: Using evolutionary to learn user preferences. In: Tan, K., Lim, M., Yao, X. and Wang, L. (eds.). Recent advances in simulated evolution and learning, pp. 20–40. World Scientific Publishing (2004)
46. Victor, P., Cornelis, C., De Cock, M. and Herrera-Viedma, E.: Practical aggregation operators for gradual trust and distrust, Fuzzy Sets and Systems, 184, 126–147 (2011)
47. Vu, H. Q., Beliakov, G., and Li, G.: A Choquet integral toolbox and its application in customers preference analysis, in Data Mining Applications with R, Elsevier (2013)
48. Yager, R.: On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Trans. on Systems, Man and Cybernetics, 18, 183–190 (1988)
49. Yager, R.: Noble Reinforcement in Disjunctive Aggregation Operators. IEEE Transactions on Fuzzy Systems, 11(6) 754–767 (2003)
50. Yager, R. R. and Filev, D. P.: Induced ordered weighted averaging operators. IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics, 20(2), 141–150 (1999)
51. Yager, R. and Rybalov, A.: Uninorm aggregation operators. Fuzzy Sets and Systems, 80, 111–120 (1996)